Benchmark: PutImageData vs DrawImage (with getImageData removed)

HTML Preparation code:

<canvas id='master' width='100' height='100'></canvas>
<canvas id='pid' width='100' height='100'></canvas>
<canvas id='di' width='100' height='100'></canvas>

Script Preparation code:

var master = document.getElementById('master');
var masterctx = master.getContext('2d');
var pid = document.getElementById('pid').getContext('2d');
var di = document.getElementById('di').getContext('2d');

masterctx.fillRect(0,0,50,50);
masterctx.fillStyle = "red";
masterctx.fillRect(50,50,100,100);

var idfull = masterctx.getImageData(0, 0, 100, 100);
var idhalf = masterctx.getImageData(25,0,75,100);

Tests:

DrawImage whole master into di canvas
di.drawImage(master,0,0);
PutImageData whole master into pid canvas
pid.putImageData(idfull,0,0);
DrawImage half master into di canvas
di.drawImage(master,25,0,50,100,25,0,50,100);
PutImageData half master into pid canvas
pid.putImageData(idhalf,25,0);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
DrawImage whole master into di canvas
PutImageData whole master into pid canvas
DrawImage half master into di canvas
PutImageData half master into pid canvas

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one month ago)

User agent: Mozilla/5.0 (X11; Linux x86_64; rv:136.0) Gecko/20100101 Firefox/136.0

Browser/OS: Firefox 136 on Linux

View result in a separate tab

Test name	Executions per second
DrawImage whole master into di canvas	690693.8 Ops/sec
PutImageData whole master into pid canvas	356884.2 Ops/sec
DrawImage half master into di canvas	244296.7 Ops/sec
PutImageData half master into pid canvas	451423.2 Ops/sec

Autogenerated LLM Summary (model gpt-4o-mini, generated one month ago):

LLMs can make mistakes. Check important info.

The benchmark defined in the provided JSON assesses the performance differences between two methods for rendering images onto HTML canvas elements in JavaScript: `putImageData` and `drawImage`. This comparison focuses on how to transfer pixel data from one canvas to another and evaluates their efficiency in various scenarios.

### Options Compared

1. **DrawImage Method:**
   - **Test Name:** "DrawImage whole master into di canvas"
     - **Benchmark Definition:** `di.drawImage(master,0,0);`
   - **Test Name:** "DrawImage half master into di canvas"
     - **Benchmark Definition:** `di.drawImage(master,25,0,50,100,25,0,50,100);`

2. **PutImageData Method:**
   - **Test Name:** "PutImageData whole master into pid canvas"
     - **Benchmark Definition:** `pid.putImageData(idfull,0,0);`
   - **Test Name:** "PutImageData half master into pid canvas"
     - **Benchmark Definition:** `pid.putImageData(idhalf,25,0);`

### Libraries Used
The benchmark is executed in the context of the HTML5 `<canvas>` API, which allows for digitally drawing graphics via JavaScript. The `getContext('2d')` method is utilized to obtain the rendering context, which provides methods for drawing shapes, text, images, and manipulating pixel data on the canvas.

### Pros and Cons of Different Approaches

#### DrawImage
- **Pros:**
  - Simplicity: This method is straightforward and intuitive for drawing images or portions of images onto a canvas without requiring manual pixel manipulation.
  - Performance: In most scenarios, using `drawImage` is optimized internally by the browser, making it faster for straightforward image transfers.
  
- **Cons:**
  - Limited Control: This method does not allow for pixel-level manipulation or direct control over individual image data pixels.

#### PutImageData
- **Pros:**
  - Pixel-Level Manipulation: Offers more control by allowing you to manipulate pixel data directly (e.g., altering colors or applying effects before placing them onto the canvas).
  
- **Cons:**
  - Performance: Generally slower than `drawImage` as it requires copying pixel data to and from a `ImageData` object. This can involve additional overhead, especially for larger images, since the data is handled at the pixel level.

### Results Summary
The benchmark results indicate the following performance in terms of executions per second for each test case:

1. **DrawImage whole master into di canvas:** 690,693.81 executions per second.  
2. **PutImageData half master into pid canvas:** 451,423.16 executions per second.
3. **PutImageData whole master into pid canvas:** 356,884.25 executions per second.
4. **DrawImage half master into di canvas:** 244,296.66 executions per second.

From the results, it's evident that for the specific test scenarios, `drawImage` significantly outperforms `putImageData` in both whole and half operations, highlighting its optimization for rapid drawing tasks.

### Other Alternatives
In addition to the tested methods (drawImage and putImageData), alternatives for image rendering might include:

- **Canvas Pre-rendering:** Use an off-screen canvas that has pre-rendered images available for quicker access during performance-critical operations.
- **WebGL:** For more complex rendering tasks, leveraging hardware acceleration via WebGL can lead to improved performance, especially when handling numerous graphical operations.
- **ImageBitmap API:** This allows for more efficient image rendering operations with less overhead by creating a bitmap representation of an image that can be drawn to the canvas more swiftly.

Choosing between these methods depends on the use case, the need for performance optimization, and the level of control required over the pixel data being drawn.

Related benchmarks:

PutImageData vs DrawImage (with getImageData removed) (version: 1)

GetImageData with PutImageData vs just DrawImage with getImageData removed

Comparing performance of: DrawImage whole master into di canvas vs PutImageData whole master into pid canvas vs DrawImage half master into di canvas vs PutImageData half master into pid canvas

Created: one month ago by: Guest

Jump to the latest result

DrawImage whole master into di canvas

PutImageData whole master into pid canvas

DrawImage half master into di canvas

PutImageData half master into pid canvas

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model gpt-4o-mini, generated one month ago):